What Are Hyperparameters and How Are They Different from Model Parameters?

 

When working with machine learning models, two important concepts often come up: model parameters and hyperparameters. While they sound similar, they are fundamentally different and play distinct roles in building and fine-tuning models. Let’s break these concepts down in a beginner-friendly way with simple examples.

 

1. What Are Model Parameters?

Model parameters are values that the model learns automatically during training. These parameters are optimized using the training data to make the model accurate.

 

Examples of Model Parameters:

In a linear regression model: Parameters include the slope (m) and intercept (b) of the line

𝑦 = 𝑚𝑥 + 𝑏

These values are learned during training to best fit the data.

 

In a neural network:

·      Weights: Numbers that determine the strength of connections between neurons.

·      Biases: Extra values added to adjust the output of neurons.

 

Key Characteristics of Model Parameters:

·      Learned automatically by the model.

·      Change dynamically as the model trains on data.

 

Example: A model adjusts weights and biases to minimize errors during training.

 

2. What Are Hyperparameters?

Hyperparameters are values set by the user (you) before training the model. Unlike parameters, hyperparameters are not learned by the model but control how the training process behaves.

 

Examples of Hyperparameters

In training neural networks:

·      Learning Rate: Controls how much the model's parameters are adjusted with each step during training.

·      Number of Layers: Determines the depth of the network.

·      Batch Size: The number of training samples processed before the model updates its parameters.

 

In decision trees:

·      Maximum Depth: How deep the tree can grow.

·      Minimum Samples per Split: The minimum number of samples required to make a split.

 

In gradient boosting models:

·      Number of Trees: How many decision trees to use in the ensemble.

·      Learning Rate: How fast the model learns.

 

 

Key Characteristics of Hyperparameters:

·      Set manually before training.

·      Do not change during the training process.

·      They control the learning process and model complexity.

 

Difference Between Model Parameters and Hyperparameters

Aspect	Model Parameters	Hyperparameters
Definition	Values learned by the model during training.	Values set by the user to guide the training process.
Examples	Weights, biases, slopes, intercepts.	Learning rate, batch size, number of layers.
Who Sets Them?	The model (via training).	The user (manually).
When Are They Set?	Adjusted dynamically during training.	Defined before training begins.
Role	Helps the model to make predictions	Controls the efficiency, complexity, and quality of training.

 

Hyperparameters significantly affect the model’s performance, training speed, and ability to generalize to unseen data. Choosing the right hyperparameters is often a trial-and-error process called hyperparameter tuning.

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